Hands-on Machine Learning Workshop

Researchers utilize high-dimensional biological data for questions ranging from identifying groups of interest to extracting biologically meaningful representations. This hands-on unsupervised machine learning workshop will introduce concepts such as cluster validation and stability, dimensionality reduction, and representation learning for transcriptomics.

About our speaker:

Jaclyn Taroni is a Principal Data Scientist at the Childhood Cancer Data Lab, an initiative of Alex's Lemonade Stand Foundation. She was a Postdoctoral Researcher in the Greene Lab at the University of Pennsylvania Perelman School of Medicine, where she worked on cross-platform normalization of gene expression data and unsupervised transfer learning for rare diseases. She conducted her thesis work in the Whitfield Lab at the Geisel School of Medicine at Dartmouth studying the rare autoimmune disease systemic sclerosis, where she developed novel frameworks for analyzing high-throughput molecular data from multiple tissues, clinical manifestations, and drug trials. (http://www.jaclyn-taroni.com/)